The single-pixel imaging(SPI) [1]} based on SPD needs to use many modulation patterns to image the object, while the operating rate of the SLM used to modulate is limited, which causes the conflict between the sampling time and the reconstructed image quality. For a moving object, the sampling time allocated to a single moving frame is very short, and combining multiple moving frames for calculation will lead to motion blur. To address this problem in SPI, a moving object can be imaged by estimating the moving speed of the object using an algorithm[2]}, [3]}, choosing the proper modulation patterns or increasing the speed of SLM to reduce the sampling time[4]}, [5]}, [6]}, [7]}, [8]}, [9]}, and estimating motion information based on low-resolution images[10]}, [11]}, [12]}. The methods by estimating motion information of the moving object are commonly used to image a moving object in recent years. Zhang et al. proposed a method to image a uniformly moving object by modifying the pattern and velocity parameters during reconstruction[2]}. Jiao et al. proposed a method to estimate the motion parameters of the object under the assumption that the type of object motion is known[3]}. In addition, many methods have been proposed to obtain the motion information of the object, such as calculating cross-correlation[10]}, [12]} or low-order moments[11]} of the images, using laterally shifting patterns[18]}, and projecting two-dimensional projective patterns[19]}. Even so, the frame rate of these methods is far lower than that of the imaging-free tracking method. Inspired by the above methods, an idea of tracking and imaging moving an object naturally appears: we can first determine the object motion information through the image-free method, and then transform the spatial-coding patterns of the object using motion information; when the number of coding patterns is sufficient, the image of moving object can be reconstructed using the compressed sensing[20]}, [21]}, [22]} algorithm. A similar idea is applied in the newest research. Guo et al. combined the geometric moment patterns and Hadamard patterns to achieve tracking and imaging of the
moving object at a frame rate of 5.55 \(~\mathrm {kHz}\)[23]} .
